An Empirical Stationary Equilibrium Search Model of the Housing Market*

Carrillo, Paul E.

doi:10.1111/j.1468-2354.2011.00677.x

Cited by 63 publications

(53 citation statements)

References 38 publications

(127 reference statements)

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“…marriage purposes [16,59], employment purposes [37] or real estate deals [14]. In such cases, agents both search and are being searched for, and their search strategies may be affected (in part) by the strategies of the other agents in the market [2,13,58].…”

Section: Two-side Searchmentioning

confidence: 99%

Exploration costs as a means for improving performance in multiagent systems

Sarne

Aumann

2014

Ann Math Artif Intell

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We consider settings were agents are faced with several possible opportunities and need to choose one. Each opportunity may offer a different utility to the agent, and determining this utility may consume resources. The underlying costly exploration problem is termed "economic search", though its essence is different from the traditional search notion in artificial intelligence (e.g. BFS, IDDFA, and A*), as there is no underlying combinatorial structure to the opportunities. We study the effects that search costs can have on individual and aggregate utility in distributed multi-agent economic-search settings. Traditionally, in such setting, search costs are regarded as a market inefficiency, and, as such, as something to be avoided or reduced to a minimum. We show, in contrast, that in many search settings, the introduction of search costs can actually improve the aggregate social welfare, or even the expected utility of each and every individual agent. We note that the proceeds from the search costs are assumed to be wasted, with no one directly benefiting from them. We demonstrate the benefits of search costs in both one-sided and two-sided search settings, using standard, classical models from economic-search theory. For the designers of multiagent systems, the results imply that deliberate (and potentially artificial) increase of search costs should be considered as possible means to improving the system's overall performance.

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Section: Two-side Searchmentioning

confidence: 99%

Exploration costs as a means for improving performance in multiagent systems

Sarne

Aumann

2014

Ann Math Artif Intell

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“…Recently, new studies have been emerging at a much faster pace and new theoretical and computational models are developing. For instance, Carrillo (2012) sets out a computationally tractable stationary equilibrium model involving housing market search. A range of studies have developed agent-based models of market search to explore information implications for landlord and tenant decisions in rental markets that shape time on market, rent and vacancy outcomes (Gilbert, Hawksworth, & Swinney, 2009;McBreen, Goffette-Nagot, & Jensen, 2011;Sun & Manson, 2010).…”

Section: Models With Searchmentioning

confidence: 99%

Housing markets, signals and search

Maclennan

O’Sullivan

2012

Journal of Property Research

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Research into the spatial structure and functioning of local housing markets typically focuses on market outcomes, particularly house price changes and household movement patterns. Explanatory models are usually based upon a standard neoclassical analysis of the housing market. That approach de-emphasises the importance of imperfect information, real market processes and the signals they generate. The inherent nature of housing means that partly informed households typically engage in search activity prior to purchasing a property. Search is inevitably a spatial process. Housing market search modelling remains relatively undeveloped. However, analysis of this process can provide important additional insights to both better explain consumer behaviour and support more informed decision-making by housing planners and market providers. We illustrate these arguments using housing search data for Scotland.

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“…In this article, I model the effect of this type of seller uncertainty on the housing market. The model adds a framework for seller uncertainty and Bayesian learning in the spirit of Lazear () to the typical features of the dynamic microsearch models in the housing literature (Salant, ; Horowitz, ; Carrillo, ). I estimate the model and use it to test whether uncertainty is important for explaining several key stylized facts about housing market dynamics that have attracted much attention in the literature, in part, because some of them are inconsistent with the predictions of standard asset pricing models.…”

Section: Introductionmentioning

confidence: 99%

Information Frictions and Housing Market Dynamics

Anenberg

2016

Int Economic Review

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This paper examines the effects of seller uncertainty over their home value on the housing market. Using evidence from a new dataset on home listings and transactions, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic search model of the home selling problem with Bayesian learning. Simulations of the estimated model show that information frictions help explain shortrun persistence in price appreciation rates and a positive (negative) correlation between price changes and sales volume (time on market).

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An Empirical Stationary Equilibrium Search Model of the Housing Market*

Cited by 63 publications

References 38 publications

Exploration costs as a means for improving performance in multiagent systems

Exploration costs as a means for improving performance in multiagent systems

Housing markets, signals and search

Information Frictions and Housing Market Dynamics

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